How to plot positions along a chromosome graphic

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星月不相逢 2021-02-05 14:16

I would like to generate a plot depicting 14 linear chromosomes for the organism I work on, to scale, with coloured bars at specified locations along each chromosome. Ideally I\

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  • 2021-02-05 14:59

    Here is a general solution for drawing these kinds of plots, adapted from this post.

    I chose to use geom_rect for this, because it allowed for more fine-tuned adjustment of the shape sizes, and allows the shapes to scale with resolution; I think that geom_segment widths do not scale.

    Also note that using this method, the marks for gene alteration locations are drawn to scale, which means they might come out so thin as to be not easily visible on the plot; you can use your discretion to adjust it to some minimum size if you want.

    Load Data

    library("ggplot2") # for the plot
    library("ggrepel") # for spreading text labels on the plot, you can replace with `geom_text` if you want
    library("scales") # for axis labels notation
    
    # insert your steps to load data from tabular files or other sources here; 
    # dummy datasets taken directly from files shown in this example
    
    # data with the copy number alterations for the sample
    sample_cns <- structure(list(gene = c("AC116366.7", "ANKRD24", "APC", "SNAPC3", 
    "ARID1A", "ATM", "BOD1L1", "BRCA1", "C11orf65", "CHD5"), chromosome = c("chr5", 
    "chr19", "chr5", "chr9", "chr1", "chr11", "chr4", "chr17", "chr11", 
    "chr1"), start = c(131893016L, 4183350L, 112043414L, 15465517L, 
    27022894L, 108098351L, 13571634L, 41197694L, 108180886L, 6166339L
    ), end = c(131978056L, 4224502L, 112179823L, 15465578L, 27107247L, 
    108236235L, 13629211L, 41276113L, 108236235L, 6240083L), cn = c(1L, 
    1L, 1L, 7L, 1L, 1L, 3L, 3L, 1L, 1L), CNA = c("loss", "loss", 
    "loss", "gain", "loss", "loss", "gain", "gain", "loss", "loss"
    )), .Names = c("gene", "chromosome", "start", "end", "cn", "CNA"
    ), row.names = c(NA, 10L), class = "data.frame")
    
    # > head(sample_cns)
    #         gene chromosome     start       end cn  CNA
    # 1 AC116366.7       chr5 131893016 131978056  1 loss
    # 2    ANKRD24      chr19   4183350   4224502  1 loss
    # 3        APC       chr5 112043414 112179823  1 loss
    # 4     SNAPC3       chr9  15465517  15465578  7 gain
    # 5     ARID1A       chr1  27022894  27107247  1 loss
    # 6        ATM      chr11 108098351 108236235  1 loss
    
    # hg19 chromosome sizes
    chrom_sizes <- structure(list(chromosome = c("chrM", "chr1", "chr2", "chr3", "chr4", 
    "chr5", "chr6", "chr7", "chr8", "chr9", "chr10", "chr11", "chr12", 
    "chr13", "chr14", "chr15", "chr16", "chr17", "chr18", "chr19", 
    "chr20", "chr21", "chr22", "chrX", "chrY"), size = c(16571L, 249250621L, 
    243199373L, 198022430L, 191154276L, 180915260L, 171115067L, 159138663L, 
    146364022L, 141213431L, 135534747L, 135006516L, 133851895L, 115169878L, 
    107349540L, 102531392L, 90354753L, 81195210L, 78077248L, 59128983L, 
    63025520L, 48129895L, 51304566L, 155270560L, 59373566L)), .Names = c("chromosome", 
    "size"), class = "data.frame", row.names = c(NA, -25L))
    
    # > head(chrom_sizes)
    #   chromosome      size
    # 1       chrM     16571
    # 2       chr1 249250621
    # 3       chr2 243199373
    # 4       chr3 198022430
    # 5       chr4 191154276
    # 6       chr5 180915260
    
    
    # hg19 centromere locations
    centromeres <- structure(list(chromosome = c("chr1", "chr2", "chr3", "chr4", 
    "chr5", "chr6", "chr7", "chr8", "chr9", "chrX", "chrY", "chr10", 
    "chr11", "chr12", "chr13", "chr14", "chr15", "chr16", "chr17", 
    "chr18", "chr19", "chr20", "chr21", "chr22"), start = c(121535434L, 
    92326171L, 90504854L, 49660117L, 46405641L, 58830166L, 58054331L, 
    43838887L, 47367679L, 58632012L, 10104553L, 39254935L, 51644205L, 
    34856694L, 16000000L, 16000000L, 17000000L, 35335801L, 22263006L, 
    15460898L, 24681782L, 26369569L, 11288129L, 13000000L), end = c(124535434L, 
    95326171L, 93504854L, 52660117L, 49405641L, 61830166L, 61054331L, 
    46838887L, 50367679L, 61632012L, 13104553L, 42254935L, 54644205L, 
    37856694L, 19000000L, 19000000L, 20000000L, 38335801L, 25263006L, 
    18460898L, 27681782L, 29369569L, 14288129L, 16000000L)), .Names = c("chromosome", 
    "start", "end"), class = "data.frame", row.names = c(NA, -24L
    ))
    
    # > head(centromeres)
    #   chromosome     start       end
    # 1       chr1 121535434 124535434
    # 2       chr2  92326171  95326171
    # 3       chr3  90504854  93504854
    # 4       chr4  49660117  52660117
    # 5       chr5  46405641  49405641
    # 6       chr6  58830166  61830166
    

    Adjust Data

    # create an ordered factor level to use for the chromosomes in all the datasets
    chrom_order <- c("chr1", "chr2", "chr3", "chr4", "chr5", "chr6", "chr7", 
                     "chr8", "chr9", "chr10", "chr11", "chr12", "chr13", "chr14", 
                     "chr15", "chr16", "chr17", "chr18", "chr19", "chr20", "chr21", 
                     "chr22", "chrX", "chrY", "chrM")
    chrom_key <- setNames(object = as.character(c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 
                                                  12, 13, 14, 15, 16, 17, 18, 19, 20, 
                                                  21, 22, 23, 24, 25)), 
                          nm = chrom_order)
    chrom_order <- factor(x = chrom_order, levels = rev(chrom_order))
    
    # convert the chromosome column in each dataset to the ordered factor
    chrom_sizes[["chromosome"]] <- factor(x = chrom_sizes[["chromosome"]], 
                                          levels = chrom_order)
    sample_cns[["chromosome"]] <- factor(x = sample_cns[["chromosome"]], 
                                         levels = chrom_order)
    centromeres[["chromosome"]] <- factor(x = centromeres[["chromosome"]], 
                                          levels = chrom_order)
    # create a color key for the plot
    group.colors <- c(gain = "red", loss = "blue")
    

    Make Plot

    ggplot(data = chrom_sizes) + 
        # base rectangles for the chroms, with numeric value for each chrom on the x-axis
        geom_rect(aes(xmin = as.numeric(chromosome) - 0.2, 
                      xmax = as.numeric(chromosome) + 0.2, 
                      ymax = size, ymin = 0), 
                  colour="black", fill = "white") + 
        # rotate the plot 90 degrees
        coord_flip() +
        # black & white color theme 
        theme(axis.text.x = element_text(colour = "black"), 
              panel.grid.major = element_blank(), 
              panel.grid.minor = element_blank(), 
              panel.background = element_blank()) + 
        # give the appearance of a discrete axis with chrom labels
        scale_x_discrete(name = "chromosome", limits = names(chrom_key)) +
        # add bands for centromeres
        geom_rect(data = centromeres, aes(xmin = as.numeric(chromosome) - 0.2, 
                                          xmax = as.numeric(chromosome) + 0.2, 
                                          ymax = end, ymin = start)) +
        # add bands for CNA value
        geom_rect(data = sample_cns, aes(xmin = as.numeric(chromosome) - 0.2, 
                                         xmax = as.numeric(chromosome) + 0.2, 
                                         ymax = end, ymin = start, fill = CNA)) + 
        scale_fill_manual(values = group.colors) +
        # add 'gain' gene markers
        geom_text_repel(data = subset(sample_cns, sample_cns$CNA == "gain"), 
                        aes(x = chromosome, y = start, label = gene), 
                        color = "red", show.legend = FALSE) +
        # add 'loss' gene markers
        geom_text_repel(data = subset(sample_cns, sample_cns$CNA == "loss"), 
                        aes(x = chromosome, y = start, label = gene ), 
                        color = "blue", show.legend = FALSE) +
        ggtitle("Copy Number Alterations") +
        # supress scientific notation on the y-axis
        scale_y_continuous(labels = comma) +
        ylab("region (bp)")
    

    Results

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  • 2021-02-05 15:04

    data

    { # dataframes
      dfChrSize<-read.table(text="chrName           chrSize
             1         640851
             2         947102
             3        1067971
             4        1200490
             5        1343557
             6        1418242
             7        1445207
             8        1472805
             9        1541735
            10        1687656
            11        2038340
            12        2271494
            13        2925236
            14        3291936", header=T)
    
      dfMarkPos<-read.table(text="chrName   markPos markSize markName
    3          817702 50000 type1
    12         1556936  50000 type2
    13         1131566  50000 type2", header=T, stringsAsFactors=F)
    }
    

    idiogramFISH plot

    install.packages("idiogramFISH")
    library(idiogramFISH) # v. 1.16.1
    
    par(mar=c(0,0,0,0) ) # b l t r
    
    plotIdiograms(dfChrSize,dfMarkPos=dfMarkPos, 
                  karIndex = FALSE,  
                  karHeight = 4,
                  orderChr = "original",
                  chrWidth = .2, 
                  chrSpacing = .5,
                  legendHeight = 2,
                  chromatids = FALSE,
                  rulerIntervalMb = 1000000,
                  useMinorTicks = TRUE,   # ruler 
                  xlimLeftMod = 2,        # modify left margin
                  ylimBotMod = -3,        # modify bottom margin
                  classMbName = "",       # chr. title
                  yPosRulerTitle = 3,     # ruler title pos.
                  xPosRulerTitle = 3)
    

    idiogramFISH coord. + ggplot

    chrAndMarksMap <- mapGGChrMark(dfChrSize,dfMarkPos,chrSpacing = .8)
    
    # ggplot
    
    library(ggplot2)
    
    ggplot() + 
      geom_polygon(aes(x=x,y=y,
                       group=Chr) 
                   ,data=chrAndMarksMap$dataChr
                   ,color="gray"
                   ,fill="gray"
      ) +
      geom_polygon(aes(x=x,y=y,
                       group=id,
                       color=markName,
                       fill=markName) 
                   ,data=chrAndMarksMap$dataMark
      ) +
      theme_classic()+
      scale_x_continuous(breaks=seq(1,nrow(dfChrSize),1)
      ) +
      scale_y_continuous(breaks = seq(0,3500000,500000),
                         labels = seq(0,3.5 , .5)
      ) +
      geom_segment(aes(y=0,yend=3500000,x=-Inf,xend=-Inf)
      )+
      theme(axis.line=element_blank(),
            axis.ticks.x = element_blank(),
            axis.title.x = element_blank(),
            axis.title.y = element_text(angle=0),
            legend.title = element_blank()
            ) +
      ylab("Mb")
    

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  • 2021-02-05 15:14

    Just save your barplot call and then call segments to make the marks at an appropriate location. E.g.:

    bp <- barplot(dat$size, border=NA, col="grey80")
    
    with(marks,
      segments(
        bp[Chromosome,]-0.5,
        Position,
        bp[Chromosome,]+0.5,
        Position,
        col=Type,
        lwd=2, 
        lend=1
       )
    )
    

    Data used:

    dat <- structure(list(chromosome = 1:14, size = c(640851L, 947102L, 
    1067971L, 1200490L, 1343557L, 1418242L, 1445207L, 1472805L, 1541735L, 
    1687656L, 2038340L, 2271494L, 2925236L, 3291936L)), .Names = c("chromosome", 
    "size"), class = "data.frame", row.names = c(NA, -14L))
    
    marks <- structure(list(Chromosome = c(3L, 12L, 13L, 5L, 11L, 14L), Position = c(817702L, 
    1556936L, 1131566L, 1041685L, 488717L, 1776463L), Type = structure(c(1L, 
    1L, 1L, 2L, 2L, 2L), .Label = c("A", "B"), class = "factor")), .Names = c("Chromosome", 
    "Position", "Type"), class = "data.frame", row.names = c(NA, 
    -6L))
    
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